{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92439c15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\missingno\\missingno.py:60: UserWarning: Plotting a sparkline on an existing axis is not currently supported. To remove this warning, set sparkline=False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m     10\u001b[0m ax \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 11\u001b[0m \u001b[43mmsno\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.25\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.25\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\missingno\\missingno.py:72\u001b[0m, in \u001b[0;36mmatrix\u001b[1;34m(df, filter, n, p, sort, figsize, width_ratios, color, fontsize, labels, sparkline, inline, freq, ax)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;66;03m# Remove extraneous default visual elements.\u001b[39;00m\n\u001b[0;32m     71\u001b[0m ax0\u001b[38;5;241m.\u001b[39mset_aspect(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 72\u001b[0m \u001b[43max0\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     73\u001b[0m ax0\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mtick_top()\n\u001b[0;32m     74\u001b[0m ax0\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mset_ticks_position(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\matplotlib\\axes\\_base.py:3194\u001b[0m, in \u001b[0;36m_AxesBase.grid\u001b[1;34m(self, visible, which, axis, **kwargs)\u001b[0m\n\u001b[0;32m   3192\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_in_list([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m], axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m   3193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m-> 3194\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvisible\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhich\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3195\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m   3196\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mgrid(visible, which\u001b[38;5;241m=\u001b[39mwhich, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\matplotlib\\axis.py:1660\u001b[0m, in \u001b[0;36mAxis.grid\u001b[1;34m(self, visible, which, **kwargs)\u001b[0m\n\u001b[0;32m   1657\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m which \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmajor\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m   1658\u001b[0m     gridkw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgridOn\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m (\u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_major_tick_kw[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgridOn\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m   1659\u001b[0m                         \u001b[38;5;28;01mif\u001b[39;00m visible \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m visible)\n\u001b[1;32m-> 1660\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_tick_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhich\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmajor\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mgridkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\matplotlib\\axis.py:932\u001b[0m, in \u001b[0;36mAxis.set_tick_params\u001b[1;34m(self, which, reset, **kwargs)\u001b[0m\n\u001b[0;32m    919\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    920\u001b[0m \u001b[38;5;124;03mSet appearance parameters for ticks, ticklabels, and gridlines.\u001b[39;00m\n\u001b[0;32m    921\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[38;5;124;03m    gridlines.\u001b[39;00m\n\u001b[0;32m    930\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    931\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_in_list([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmajor\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mminor\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m], which\u001b[38;5;241m=\u001b[39mwhich)\n\u001b[1;32m--> 932\u001b[0m kwtrans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_translate_tick_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    934\u001b[0m \u001b[38;5;66;03m# the kwargs are stored in self._major/minor_tick_kw so that any\u001b[39;00m\n\u001b[0;32m    935\u001b[0m \u001b[38;5;66;03m# future new ticks will automatically get them\u001b[39;00m\n\u001b[0;32m    936\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reset:\n",
      "File \u001b[1;32mc:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\matplotlib\\axis.py:1076\u001b[0m, in \u001b[0;36mAxis._translate_tick_params\u001b[1;34m(kw, reverse)\u001b[0m\n\u001b[0;32m   1074\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m kw_:\n\u001b[0;32m   1075\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allowed_keys:\n\u001b[1;32m-> 1076\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1077\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeyword \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not recognized; valid keywords are \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1078\u001b[0m             \u001b[38;5;241m%\u001b[39m (key, allowed_keys))\n\u001b[0;32m   1079\u001b[0m kwtrans\u001b[38;5;241m.\u001b[39mupdate(kw_)\n\u001b[0;32m   1080\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m kwtrans\n",
      "\u001b[1;31mValueError\u001b[0m: keyword grid_b is not recognized; valid keywords are ['size', 'width', 'color', 'tickdir', 'pad', 'labelsize', 'labelcolor', 'zorder', 'gridOn', 'tick1On', 'tick2On', 'label1On', 'label2On', 'length', 'direction', 'left', 'bottom', 'right', 'top', 'labelleft', 'labelbottom', 'labelright', 'labeltop', 'labelrotation', 'grid_agg_filter', 'grid_alpha', 'grid_animated', 'grid_antialiased', 'grid_clip_box', 'grid_clip_on', 'grid_clip_path', 'grid_color', 'grid_dash_capstyle', 'grid_dash_joinstyle', 'grid_dashes', 'grid_data', 'grid_drawstyle', 'grid_figure', 'grid_fillstyle', 'grid_gapcolor', 'grid_gid', 'grid_in_layout', 'grid_label', 'grid_linestyle', 'grid_linewidth', 'grid_marker', 'grid_markeredgecolor', 'grid_markeredgewidth', 'grid_markerfacecolor', 'grid_markerfacecoloralt', 'grid_markersize', 'grid_markevery', 'grid_mouseover', 'grid_path_effects', 'grid_picker', 'grid_pickradius', 'grid_rasterized', 'grid_sketch_params', 'grid_snap', 'grid_solid_capstyle', 'grid_solid_joinstyle', 'grid_transform', 'grid_url', 'grid_visible', 'grid_xdata', 'grid_ydata', 'grid_zorder', 'grid_aa', 'grid_c', 'grid_ds', 'grid_ls', 'grid_lw', 'grid_mec', 'grid_mew', 'grid_mfc', 'grid_mfcalt', 'grid_ms']"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 510,
       "width": 831
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PATH = r\"./Data/train.csv\"\n",
    "df = pd.read_csv(PATH)\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "msno.matrix(df,ax=ax,color=(0.25,0.25,0.5))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13eeb2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 597,
       "width": 831
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reset -f # 清除变量\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# 加载数据\n",
    "PATH = r\"./Data/train.csv\"\n",
    "df = pd.read_csv(PATH)\n",
    "\n",
    "# 绘制缺失值矩阵\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.isnull(), cbar=False, cmap=\"viridis\")\n",
    "plt.title(\"Missing Values Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "03b772c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\missingno\\missingno.py:60: UserWarning: Plotting a sparkline on an existing axis is not currently supported. To remove this warning, set sparkline=False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检测到不兼容的关键字参数 'grid_b'，尝试修改代码...\n",
      "尝试修改代码后仍然出现错误: grid() got multiple values for argument 'visible'\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 510,
       "width": 831
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reset -f\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "PATH = r\"./Data/train.csv\"\n",
    "df = pd.read_csv(PATH)\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "try:\n",
    "    # 尝试正常调用 msno.matrix\n",
    "    msno.matrix(df, ax=ax, color=(0.25, 0.25, 0.5))\n",
    "except ValueError as e:\n",
    "    if \"keyword grid_b is not recognized\" in str(e):\n",
    "        print(\"检测到不兼容的关键字参数 'grid_b'，尝试修改代码...\")\n",
    "        try:\n",
    "            # 手动修改 missingno 库中相关代码逻辑（这里模拟修改）\n",
    "            # 假设我们可以直接修改 ax 的 grid 调用逻辑\n",
    "            import inspect\n",
    "            import types\n",
    "\n",
    "            # 获取原始的 msno.matrix 函数\n",
    "            original_matrix = msno.matrix\n",
    "\n",
    "            def modified_matrix(*args, **kwargs):\n",
    "                ax = kwargs.get('ax')\n",
    "                if ax:\n",
    "                    # 备份原始的 grid 方法\n",
    "                    original_grid = ax.grid\n",
    "\n",
    "                    def new_grid(*grid_args, **grid_kwargs):\n",
    "                        # 移除不兼容的关键字参数\n",
    "                        grid_kwargs.pop('b', None)\n",
    "                        grid_kwargs['visible'] = False\n",
    "                        return original_grid(*grid_args, **grid_kwargs)\n",
    "\n",
    "                    # 替换 ax 的 grid 方法\n",
    "                    ax.grid = types.MethodType(new_grid, ax)\n",
    "                return original_matrix(*args, **kwargs)\n",
    "\n",
    "            # 使用修改后的函数\n",
    "            msno.matrix = modified_matrix\n",
    "            msno.matrix(df, ax=ax, color=(0.25, 0.25, 0.5))\n",
    "        except Exception as new_e:\n",
    "            print(f\"尝试修改代码后仍然出现错误: {new_e}\")\n",
    "    else:\n",
    "        print(f\"出现其他 ValueError 错误: {e}\")\n",
    "except Exception as general_e:\n",
    "    print(f\"出现未知错误: {general_e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "803601f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lenovo\\miniconda3\\envs\\env_py38\\lib\\site-packages\\missingno\\missingno.py:60: UserWarning: Plotting a sparkline on an existing axis is not currently supported. To remove this warning, set sparkline=False.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 618,
       "width": 920
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%reset -f\n",
    "%config InlineBackend.figure_format = \"retina\"\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "import types\n",
    "PATH = r\"./Data/train.csv\"\n",
    "df = pd.read_csv(PATH)\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# 自定义 matrix 函数，处理兼容性问题\n",
    "def custom_matrix(df, ax, color):\n",
    "    try:\n",
    "        msno.matrix(df, ax=ax, color=color)\n",
    "    except ValueError as e:\n",
    "        if \"keyword grid_b is not recognized\" in str(e):\n",
    "            # 保存原始的 grid 方法\n",
    "            original_grid = ax.grid\n",
    "            # 定义一个新的 grid 方法，替换关键字参数\n",
    "            def new_grid(visible=None, which='major', axis='both', **kwargs):\n",
    "                if 'b' in kwargs:\n",
    "                    visible = kwargs.pop('b')\n",
    "                return original_grid(visible=visible, which=which, axis=axis, **kwargs)\n",
    "            # 替换 ax 的 grid 方法\n",
    "            ax.grid = types.MethodType(new_grid, ax)\n",
    "            msno.matrix(df, ax=ax, color=color)\n",
    "        else:\n",
    "            raise e\n",
    "\n",
    "custom_matrix(df, ax=ax, color=(0.25, 0.25, 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44eb902a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toyota Corolla started.\n",
      "Engine with 120 horsepower started.\n",
      "Toyota Corolla stopped.\n",
      "Engine stopped.\n",
      "<__main__.Engine object at 0x00000259499F2F10>\n",
      "Toyota Corolla\n"
     ]
    }
   ],
   "source": [
    "## 类的组合关系\n",
    "class Engine:\n",
    "    def __init__(self, horsepower):\n",
    "        self.horsepower = horsepower\n",
    "\n",
    "    def start(self):\n",
    "        print(f\"Engine with {self.horsepower} horsepower started.\")\n",
    "\n",
    "    def stop(self):\n",
    "        print(\"Engine stopped.\")\n",
    "\n",
    "class Car:\n",
    "    def __init__(self, make, model, horsepower):\n",
    "        self.make = make\n",
    "        self.model = model\n",
    "        self.engine = Engine(horsepower)\n",
    "\n",
    "    def start(self):\n",
    "        print(f\"{self.make} {self.model} started.\")\n",
    "        self.engine.start()\n",
    "\n",
    "    def stop(self):\n",
    "        print(f\"{self.make} {self.model} stopped.\")\n",
    "        self.engine.stop()\n",
    "\n",
    "# 创建一个 Car 实例\n",
    "my_car = Car(\"Toyota\", \"Corolla\", 120)\n",
    "\n",
    "# 启动汽车\n",
    "my_car.start()\n",
    "\n",
    "# 停止汽车\n",
    "my_car.stop()\n",
    "print(my_car.engine)\n",
    "print(my_car.make,my_car.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceb39261",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Animal:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "\n",
    "    def speak(self):\n",
    "        pass\n",
    "\n",
    "class Dog(Animal):\n",
    "    def speak(self):\n",
    "        return \"Woof!\"\n",
    "\n",
    "class Cat(Animal):\n",
    "    def speak(self):\n",
    "        return \"Meow!\"\n",
    "\n",
    "dog = Dog(\"Buddy\")\n",
    "cat = Cat(\"Kitty\")\n",
    "\n",
    "print(dog.name)  # 输出: Buddy\n",
    "print(dog.speak())  # 输出: Woof!\n",
    "print(cat.name)  # 输出: Kitty\n",
    "print(cat.speak())  # 输出: Meow!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db64f1be",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Shape:\n",
    "    def __init__(self, color):\n",
    "        self.color = color\n",
    "\n",
    "    def area(self):\n",
    "        pass\n",
    "\n",
    "class Rectangle(Shape):\n",
    "    def __init__(self, color, width, height):\n",
    "        super().__init__(color)\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "\n",
    "    def area(self):\n",
    "        return self.width * self.height\n",
    "\n",
    "class Circle(Shape):\n",
    "    def __init__(self, color, radius):\n",
    "        super().__init__(color)\n",
    "        self.radius = radius\n",
    "\n",
    "    def area(self):\n",
    "        return 3.14 * self.radius * self.radius\n",
    "\n",
    "rectangle = Rectangle(\"blue\", 10, 5)\n",
    "circle = Circle(\"red\", 7)\n",
    "\n",
    "print(rectangle.color)  # 输出: blue\n",
    "print(rectangle.area())  # 输出: 50\n",
    "print(circle.color)  # 输出: red\n",
    "print(circle.area())  # 输出: 153.86\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
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